White Matter Correlates with Language Outcomes in Moderate to Late Preterm and Early Term Infants
Poster No:
1034
Submission Type:
Abstract Submission
Authors:
Xiujuan Geng1, Peggy Chan1, Winnie Chu1, Hugh Lam1, Patrick Wong1
Institutions:
1The Chinese University of Hong Kong, Hong Kong, NA
First Author:
Co-Author(s):
Introduction:
Moderate to late preterm birth (MLPT) has found to be associated with language developmental delay or deficits. It is not clear how the preterm-related neural pathologies are linked to language development. Previous studies reported white matter differences in very preterm and MLPT infants (Dibble et al 2021) and white matter microstructure correlations with later language outcomes in very preterm children (Vassar et al 2020). We have recently showed that hypomyelination is linked to worse speech encoding in preterm and term babies (Novitskiy et al 2023). In the present study, we further hypothesize that language-associated white matter pathways are affected by prematurity. Importantly, maturation levels around birth are prospectively correlated with later language ability.
Methods:
One hundred and seven healthy Chinese MLPT and term infants (mean [range] gestation age (GA) at birth: 36.6 [32.0-39.1] weeks) were recruited. Infant's language ability, including expressive, receptive and composite scores were assessed at 12 months old using Bayley Scales of Infant and Toddler Development III. Subjects were scanned at approximate two months of chronological age (mean [range]: 9.7 [3.0-18.6] weeks) and during natural sleep on a Siemens Magnetom Prisma scanner. Diffusion-weighted sequences were collected with 46 volumes at multiple b-values (500-2000 s/mm2) and a voxel size of 1.6x1.6x1.8 mm3. Fractional anisotropy (FA) and radial diffusivity (RD) were estimated and spatially normalized to standard space using unbiased nonlinear registration (Geng et al 2024). Tract-based spatial statistics (TBSS) was conducted and statistical analysis was applied on entire brain. Partial correlation was performed to test whether white matter at birth was correlated with language at 1-year-old, while controlling sex, GA, and age at assessment. SVM was conducted to explore the predictive power of white matter.
Results:
Correlational analyses show that expressive language scores at 1-year-old were significantly positively correlated with FA values in areas along several white matter tracts including left external capsule, uncinate fasciculus, inferior fronto-occipital fasciculus, posterior and retrolenticular part of internal capsule, bilateral superior corona radiata, genu, body and splenium of corpus callosum (Fig. 1). Expressive language scores was negatively correlated with RD values in prefrontal, parietal and temporal regions.
Prediction results of language outcomes at 1-year-old using white matter and/or demographic information were showed in Fig. 2. Compared to chance, brain only, and brain together with demographics predict language with an accuracy of 0.72 and sensitivity of 0.96, higher than the prediction using demographics (sex, age at assessment, & GA) with an accuracy of 0.62 and sensitivity of 0.76. Note: language outcomes were categorized by low (below 16th percentile, i.e., 1 standard deviation below average) vs normal (the rest).
Prediction results of language outcomes at 1-year-old using white matter and/or demographic information were showed in Fig. 2. Compared to chance, brain only, and brain together with demographics predict language with an accuracy of 0.72 and sensitivity of 0.96, higher than the prediction using demographics (sex, age at assessment, & GA) with an accuracy of 0.62 and sensitivity of 0.76. Note: language outcomes were categorized by low (below 16th percentile, i.e., 1 standard deviation below average) vs normal (the rest).
Conclusions:
White matter maturation was correlated with 1-year-old language outcomes on tracts including language pathways such as external capsule, superior corona radiata, uncinate fasciculus, and inferior fronto-occipital fasciculusmeasured near birth (Houston et al 2019). Moreover, white matter showed high prediction accuracy (~0.72) and sensitivity (~0.96) of language abilities at 1-year-old. Our next step is to test the hypothesis that the preterm babies who fail to reach term equivalent white matter maturation status are the ones with language developmental delays/deficits.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
Language
White Matter
Other - Preterm Bith
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Vassar R, Schadl K, Cahill-Rowley K, Yeom K, Stevenson D, Rose J. (2020). Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born Very Preterm. Pediatr Neurol.108:86-92.
Novitskiy N, Chan PHY, Chan M, Lai CM, Leung TY, Leung TF, Bornstein MH, Lam HS, Wong PCM. (2023). Deficits in neural encoding of speech in preterm infants. Dev Cogn Neurosci. 61:101259.
Geng X, Chan PH, Lam HS, Chu WC, Wong PC. (2024). Brain templates for Chinese babies from newborn to three months of age. Neuroimage. 1;289:120536.
Houston J, Allendorfer J, Nenert R, Goodman AM, Szaflarski JP. (2019). White Matter Language Pathways and Language Performance in Healthy Adults Across Ages. Front Neurosci. 1;13:1185.